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无人机与人工智能正在彻底改变野生动物监测与保护工作。

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation.

作者信息

Gonzalez Luis F, Montes Glen A, Puig Eduard, Johnson Sandra, Mengersen Kerrie, Gaston Kevin J

机构信息

Australian Research Centre for Aerospace Automation (ARCAA), Queensland University of Technology (QUT), 2 George St, Brisbane QLD 4000, Australia.

ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS), Queensland University of Technology (QUT), 2 George St, Brisbane QLD 4000, Australia.

出版信息

Sensors (Basel). 2016 Jan 14;16(1):97. doi: 10.3390/s16010097.

Abstract

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

摘要

对受威胁物种和入侵物种进行调查以获得准确的种群估计是一项重要但具有挑战性的任务,需要投入大量的时间和资源。使用现有的地面监测技术(如相机陷阱和徒步调查)进行估计,已知资源密集、可能不准确且不精确,并且难以验证。无人驾驶飞行器(UAV)、人工智能和小型化热成像系统的最新发展为野生动物专家提供了一个新机会,可以以低成本对相对较大的区域进行调查。本文介绍的系统包括热图像采集以及用于在森林或开阔区域对野生动物进行目标检测、分类和跟踪的视频处理管道。该系统在来自地面和试飞视频的热视频数据上进行了测试,结果发现它能够检测出调查区域内所有目标野生动物。该系统具有灵活性,用户可以轻松定义要分类的对象类型以及分类过程中应考虑的对象特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3b/4732130/6feabb096b8e/sensors-16-00097-g001.jpg

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